Short-term traffic speed prediction and forecasting using machine learning analysis of spatiotemporal traffic speed dependencies in probe and weather data

ABSTRACT

A framework for modeling traffic speed in a transportation network analyzes both the spatial and temporal dependencies in probe-based traffic speeds, historical weather data, and forecasted weather data, using multiple machine learning models. A decentralized partial least squares (PLS) regression model predicts short-term speed using localized, historical probe-based traffic data, and a deep learning model applies the predicted short-term speed to further estimate traffic speed at specified times and at specific locations in the transportation network for predicting traffic bottlenecks and other future traffic states.

FIELD OF THE INVENTION

The present invention relates generally to the field of trafficmanagement. More specifically, the present invention relates to systemsand methods of analyzing link-based speed data and weather data usingmachine learning models to estimate traffic speed, and providepredictions and forecasts of traffic conditions based on estimates oftraffic speed at specific locations and at specific times within atransportation network.

BACKGROUND OF THE INVENTION

Traffic speed monitoring is a major issue in today's urban communities,and there are many approaches for monitoring, analyzing, andcharacterizing traffic speed within transportation networks and thevarious links that comprise such networks. These approaches make use oflarge amounts of available traffic data, which has made it possible toanalyze and control urban traffic more effectively. Traffic congestionprediction, and specifically short-term speed and travel timeprediction, has attracted significant attention because it can bereadily provided to roadway users through Advanced Traveler InformationSystems (ATIS) and through mobile applications on user computingdevices. Short-term traffic information can also be utilized by trafficmanagement centers to control traffic in a more dynamic and responsivemanner. Despite these advances however, it remains that traffic stateprediction still needs improvement in terms of accuracy, computationalefficiency, and scalability.

Traffic data science, which includes the study of these large amounts ofavailable traffic data, has matured and expanded to now include agreater number of input data sources that are used to train and deploypredictive models. Although much research has gone into developingapplications of short-term traffic state prediction, there has beenlittle emphasis on the importance of particular types of input datasources in this domain, and their spatio-temporal impact on trafficstates, for example the importance of weather-related data analytics.Currently-available techniques for traffic speed monitoring do notaccurately utilize weather data attributes (e.g. precipitation, windspeed, visibility, etc.) together with the traffic speed data inprediction of future traffic states. Other types of data sources mayalso be important for accurately predicting future traffic states, suchas incident data and its spatial and temporal impact on traffic speed.Current approaches in traffic data science therefore do not fullyaccount for dependencies across time and space that affect conditionssuch as traffic speed

Traffic bottlenecks are one example where more accurate predictionsbased on traffic speed dependencies can produce improvements intransportation management. Bottlenecks are locations on a roadway wherethere is a temporary or permanent drop in capacity (defined as themaximum number of vehicle throughput per second) compared toimmediately-adjacent downstream locations on the same roadway. Thedetection and removal of bottlenecks is important for organizationsresponsible for managing transportation networks, such as statedepartments of transportation or private entities operating toll roads.Bottlenecks can lead to traffic congestion and the formation of a queueof slow or stopped vehicles behind, or upstream of, the bottleneck.These organizations have an interest in knowing where a bottleneck willoccur, how long the queue of vehicles behind it will be, and how long itwill persist over a given period of time.

Traffic flow forecasting and short-term traffic state prediction generalinvolves the application of mathematical formulas and models. Theseinclude the use of time series models (such as autoregressive movingaverage (ARMA) and autoregressive integrated moving average (ARIMA)models) and statistical approaches, such as regression models toestimate link travel time using inductive loop detector data, based onboth real-time and historical link travel time profiles. For example,linear regression has been used to predict the nonlinear time series oftravel time; such regression models however require storing too manyparameters, and need to be periodically retrained.

Other commonly-used techniques used in traffic state prediction arenon-parametric approaches, such as a Kalman filter and a k-nearestneighbor (k-NN) algorithm, as well as machine learning approaches, suchas decision trees and single vector machine (SVM), but none of thesefully account for either the spatial or the temporal nature of trafficspeed dependency, and therefore do not provide traffic managers with areliable analysis of traffic states when used to make predictions. Othermachine learning techniques have been explored to predict link-basedcongestion that attempt to solve for spatial and temporal dependencies,such as neural networks and models that attempt to apply deep learningtechniques within such networks; these include Restricted BoltzmanMachines and neural network variations such as Recurrent Neural Networks(RNNs), where the congestion is a binary variable based on the fixedspeed threshold. However, in these models the matrix rows of link-basedspeed data are independent, such that when generally applied the modelsdo not account for the interaction between adjacent roadway segments orthe impact of traffic combinations across different links.

Still other machine learning approaches include Convolutional NeuralNetworks (CNNs), which consider the immediate past and a large volume ofhistorical data, and long short-term memory (LSTM) networks, whichcapture long-term temporal dependency for short-term speed prediction,but do not consider the spatial dependency of traffic segments.Regardless of the specific type, machine learning approaches requirelarge amounts of training data in order to obtain reasonable predictionperformance, particularly when the problems involve complex dynamicalsystems. Such approaches also require large amounts of computationalresources at least in processing capacity and bandwidth in order to makesuch predictions, and therefore have not been widely applied to a degreeof success warranting widespread utility.

Accordingly, there is a need in the existing art for improvements intraffic-related data science that are able to model different types ofdata to produce more accurate predictions of future traffic states fortransportation management. There is a further need in the existing artfor improvements in modeling historical and real-time data forpredicting future traffic states that accounts for both spatial andtemporal dependencies in traffic speed, and still a further need forimprovements in such modeling to account for large volumes of data whilestill being computationally efficient and producing reliable,actionable, and timely traffic predictions.

BRIEF SUMMARY OF THE INVENTION

Traffic problems, such as congestion due to bottlenecks, often aggravateand/or influence traffic conditions within a wider transportationnetwork comprised of multiple links, and are often not limited to asingle segment or even a linear sequence of links. Traffic bottlenecksare directly relative to vehicular speed, and therefore slowdowns intraffic speed over time, combined with the spatial impact acrossmultiple links or segments, means that traffic speed must be analyzedacross both time and space to produce more accurate predictions oftraffic conditions and resulting states of a transportation network.

The present invention provides a traffic speed modeling framework forprecision traffic analysis, for predicting traffic speed and forecastingtraffic states within transportation networks. This traffic speedmodeling framework is provided in one or more systems and methods fordeveloping short-term traffic predictions of traffic speed based atleast on previously-recorded link-based traffic data and weather data.The traffic speed modeling framework applies machine learningalgorithms, and deep learning techniques within such algorithms, toassess spatio-temporal dependencies affecting traffic speed in suchtraffic data and weather data to generate predictions of traffic speedthat are used to predict the occurrence of bottlenecks and forecastfuture traffic states.

The traffic speed modeling framework applies a multi-part dataprocessing approach in which a partial least squares regression (PLS) isused to model the predictor space, by constructing a predictors matrixthat maps historical speed to future traffic speed to assess theshort-term speed for a given link. The framework then applies the outputof this matrix to a deep learning model comprised of neural networkshaving an encoder-decoder sequence-to-sequence architecture to analyzespatial and temporal dependencies impacting the short-term speed, andcalculate traffic speed at each link of the transportation network fromthis deep learning model. This output is then applied to generatepredictions of congestion-related conditions such as trafficbottlenecks, and forecast future traffic states within thetransportation network.

It is therefore one objective of the present invention to providesystems and methods of assessing traffic speed within a transportationnetwork. It is another objective of the present invention to providesystems of methods of assessing traffic speed using varied data sourcesthat enable accounting for spatial and temporal dependencies in trafficspeed. It is another objective of the present invention to providesystems and methods of assessing traffic speed that produce reliable,accurate, and timely predictions of traffic speed based on such spatialand temporal dependencies. It is yet a further objective of the presentinvention to provide systems and methods of assessing traffic speed thatincorporate a processing framework that includes a regression model anda deep learning model for extracting such reliable, accurate, and timepredictions from the multiple types of input data being analyzed.

It is still a further objective to provide a framework for utilizingpredictions of traffic speed to assess future traffic states within sucha transportation network. It is yet a further objective to provide aframework for estimating traffic speed, and for generating predictionsof traffic bottlenecks, and other forecasts of future traffic states atspecified times, within such a transportation network.

Other objects, embodiments, features and advantages of the presentinvention will become apparent from the following description of theembodiments, taken together with the accompanying drawings, whichillustrate, by way of example, the principles of the invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of theinvention and together with the description, serve to explain theprinciples of the invention.

FIG. 1 is a systemic diagram illustrating elements of a traffic speedmodeling framework according to the present invention;

FIG. 2 is a flowchart of steps in a process of performing the trafficspeed modeling framework according to one embodiment of the presentinvention; and

FIG. 3A and FIG. 3B are schematic illustrations of asequence-to-sequence architecture for a deep learning model forestimating traffic speed in a transportation network, according to oneembodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the present invention, reference is madeto the exemplary embodiments illustrating the principles of the presentinvention and how it is practiced. Other embodiments will be utilized topractice the present invention and structural and functional changeswill be made thereto without departing from the scope of the presentinvention.

The present invention, as noted above, provides a traffic speed modelingframework 100 that is embodied in one or more systems and methods forpredicting speed in a transportation network 102, and for utilizing suchtraffic speed predictions to further predict and forecast trafficstates, such as bottlenecks, at specific locations and at specifiedtimes within such a transportation network 102. The traffic speedmodeling framework 100 analyzes input data 110 that at least includeslocalized, historical link-based speed information and weatherinformation for one or more geographical areas comprising thetransportation network 102, and applies this input data 110 to a set ofmachine learning-based models configured to estimate traffic speed 160and realize improvements in traffic conditions within the transportationnetwork 102.

FIG. 1 shows a system diagram of traffic speed modeling framework 100,which is performed within one or more systems and/or methods thatincludes several components, each of which define distinct activitiesand functions for processing input data 110 that at least includeslink-based probe data 111 and weather data 113. The traffic speedmodeling framework 100 predicts short-term traffic speed 148 for eachlink 104, and estimates traffic speed 160 for specific locations and atspecific times, by performing various mathematical calculations andexecuting various algorithms within the set of machine learning-basedmodels, and further generates predictions and forecasts of trafficstates within a transportation network 102.

The traffic speed modeling framework 100 ingests, receives, requests, orotherwise obtains the input data 110 from a plurality of differentsources, which may be either proprietary capturing systems or trafficdetection systems, third-party sources, or both. For example, link-basedprobe data 111 may be obtained by one or more capturing sensors, such asthose positioned in or near a roadway or traffic intersection. Suchsensors may include, for example, ranging or radar systems, imagingsystems such as cameras (including RGB, video, or thermal cameras),magnetometers, acoustic sensors, loops, ultrasonic sensors,piezoelectric sensors, air pressure tubes, and any other sensors,devices or systems which are capable of detecting a presence of objectsand for accumulating speed information, and data obtained from suchsensors may be provided via calls to one or more application programminginterfaces (APIs) 136. Link-based probe data 111 may also be provided bythird-party sources, and such information may be ingested into thetraffic speed modeling framework 100 in many different wants, forexample via one or more API 136 calls. Similarly, weather data 113 mayalso be collected by proprietary systems or provided by third-partysources as described further herein, and ingested into the traffic speedmodeling framework 100 in many different ways, again for example via oneor more API 136 calls.

Link-based probe data 111 represents traffic speed 112 for localizedlinks 104 of a transportation network 102. Links 104 represent roadwaysegments that comprise a transportation network 102, and probes, asnoted below, may be sensors or other capturing systems which sense orotherwise collect information pertaining to traffic speed 112 for eachlink 104. Weather data 113 includes meteorological and climatologicalinformation for a geographical area 106 that comprises a particulartransportation network 102, and may represent historical weatherinformation 114, predicted and/or forecasted weather information 115representative of assessments of localized weather conditions such aslong-range climatological and/or meteorological forecasting provided byone or more predictive weather models, and real-time or current weatherinformation 116.

Long-range climatological and/or meteorological forecasting may begenerated by numerical weather prediction models, dynamical extendedrange weather forecast models that may involve multiple dataassimilation systems and forecasting systems, or a combination of suchmodels and systems. For example, data assimilation systems may be usedto provide historic and current atmospheric and land surface initialconditions and also global ocean temperatures, and forecasting systemsmay incorporate the U.S. National Centers for Environmental Predictions(NCEP) Global Forecast System (GFS) for atmospheric predictions and theGeophysical Fluid Dynamics Laboratory Modular Ocean Model to providesea-surface temperature predictions. Software and data supporting theabove are publicly available from the NCEP.

Also, numerical weather prediction models (NWP) and/or surface networksmay be combined with data from weather radars and satellites toreconstruct the current and near-term forecasted weather conditions onany particular area to be analyzed. There are numerous industry NWPmodels available, and any such models may be used as sources ofmeteorological data in the present invention. Examples of NWP models atleast include RUC (Rapid Update Cycle), WRF (Weather Research andForecasting Model), GFS (Global Forecast System) (as noted above), andGEM (Global Environmental Model). Meteorological data is received inreal-time, and may come from several different NWP sources, such as fromthe European Centre for Medium-Range Weather Forecasting (ECMWF),Meteorological Services of Canada's (MSC) Canadian Meteorological Centre(CMC), as well as the National Oceanic and Atmospheric Administration's(NOAA) Environmental Modeling Center (EMC), and many others. The presentinvention may be configured to ingest weather data 113 from all types ofNWP models, regardless of whether publicly, privately, or internallyprovided or developed.

Still further, real-time or current weather information 116 may berepresentative of assessments of localized weather conditions, and maybe produced by many different sources of weather data 113 to provide oneor more of observed weather data and current analyses of observedweather data, and predicted weather data, for example as data that iscomplementary to the data assimilation systems and forecasting systemsnoted above. Such additional sources of weather data may include datafrom both in-situ and remotely-sensed observation platforms.

Input data 110 may also include other traffic data elements thatrepresent traffic speed-related information in a transportation network102. For example, input data 110 may include incident data 117, whichmay or may not be provided by or derived from systems such as sensors.Incident data 117 may include information pertaining to non-movingobjects within a roadway represented by a link 104 in the transportationnetwork 102, that can cause abnormal pedestrian and/or vehiclemovements, such as prone objects or pedestrians that may have fallen tothe pavement. Other types of incidents include maintenance activitiessuch as work zones (and vehicles present in the roadway for performingsuch activities), and a presence of unauthorized vehicles. Other sourcesof incident data 117 in addition to capture systems such as sensorsinclude observation data that is provided directly by users inreal-time, for example via applications resident on mobile devices, suchas those associated with social media platforms. Still further, softwareinstalled on user devices may also provide such incident data 117 (orinformation from which incident data 117 may be derived), for examplesoftware providing GPS probe data and Bluetooth data; such data may beindicative of traffic conditions in or around a work zone within whichmaintenance activity is being conducted. Incident data 117 may thereforeinclude, or be derived from, one or more sets of probe data provided byglobal positioning system devices, and data collected by a plurality ofpassive Bluetooth devices configured to read one or more of MACaddresses signal strength indicators emitted from Bluetooth devices invehicles near where maintenance activity is or will occur.

Input data 110 may further include more static speed-related data forthe roadway representing a link 104. This may include a posted speedlimit, or an average historical speed for a particular location at aparticular time; such information may be utilized, for example, tocompare traffic speed estimates for the particular location and theparticular time to anticipate congestion levels within thetransportation network 102 as a forecast of a traffic state 180, asnoted below.

Regardless of type, input data 110 is applied to a plurality of dataprocessing elements 132 in the traffic speed modeling framework 100 thatare components within a computing environment 130 that also includes oneor more processors 133 and a plurality of software and hardwarecomponents. The one or more processors 133 and plurality of software andhardware components are configured to execute program instructions orroutines to perform the mathematical functions, algorithms, machinelearning, and other analytical approaches comprising the data processingfunctions described herein, and embodied within the plurality of dataprocessing elements 132.

The plurality of data processing elements 132 include a data collectionand initialization element 134 that is configured to ingest, receive,request, or otherwise obtain the input data 110 as noted above, andinitialize the information represented therein for further processingwithin the traffic speed modeling framework 100. The plurality of dataprocessing elements 132 also include, as noted above, a set of machinelearning-based models, such as a partial-least-squares regression model140, configured to execute one or more algorithms that predict 148short-term traffic speed for each link 104 based on historical trafficspeed 112, and a deep learning model 150 configured to execute one ormore algorithms that generates a further prediction and forecast as anestimated traffic speed 160 at specific locations within thetransportation network 102 and at specified times.

The machine learning-based, partial least squares (or PLS) regressionmodel 140 is comprised of one or more machine learning algorithms thatare configured to capture the spatial and temporal dependencies oftraffic speed 112 in the link-based probe data 111, and accurately mapand correlate a historical speed to an estimate of a future speed. Thesespatial and temporal dependencies are analyzed to predict 148 short-termtraffic speed for each link 104, and develop a training dataset for thedeep learning model(s) 150 that further adds weather data 113 and anyother relevant information to generate such an estimate of future speed.

To predict 148 the short-term traffic speed for a given link 104, thePLS regression model 140 first selects link-based predictors 142 fromrelevant features in a feature space representing each link 104 in thetransportation network 102 being modeled. The present invention thenconstructs a matrix 144, represented by X below and defined by a numberof time steps and the selected link-based predictors for each link 104.Such a predictors matrix 144 may be represented asX∈R ^(nd×(mnt))

and is constructed using the historical speed data for m local links 104over nd days (including nt time steps in each day):X=(X1X2 . . . Xm)

where X^(k) for every k∈{1, 2, . . . , m} is the traffic data matrix forlink k.

Each row of the matrix 144 corresponds to a specific day in the dataset,and each column corresponds to the recorded data for link k for acertain time of the day. The matrix 144 generates a response (output)variable Y∈R^(nd×nt) in a response dataset 146 which represents speedfor the next nτ time steps is defined in the same manner, except that itcontains data for the prediction link 104 only.

The PLS regression model 140 applies a dimension reduction method thattransforms the predictors and then fits a least-squares model using thetransformed predictor variables, and is particularly helpful where thenumber of predictors (in this case, the variable X, which contains datafrom multiple links 104) is too large and greater than the number ofobservations, where using other techniques such as multiple linearregression would result in overfitting. The use of a partial leastsquares approach in the regression model 140 enables decomposition ofthe predictor matrix X and response dataset consisting of variables Yinto new variables (which may be referred to as score matrices) betweenwhich the regression is performed.

The score matrices can be represented as T and U respectively, thedecomposition is performed as follows:X=TP ^(T) +E _(x)Y=UQ ^(T) +E _(y)where P and Q are the orthogonal loading matrices, and Ex and Ey are thematrices of residuals. The algorithm is computed in an iterative manner.It finds weight vectors w and c to create a linear combination of thecolumns of X and Y such that cov(t,u) is maximal, where the scorevectors t_(i) and u_(i) are defined as t_(i)=Xw_(i) and u_(i)=Y_(ci).

PLS-based prediction in the regression model 140 is a decentralizedapproach, in which each link 104 predicts its own future speed based onlocal, historical speed 112. For every link 104 and each predictionperiod, the input matrix, X∈Rn^(d×(mn) ^(t) ⁾, is constructed usingtraffic speed 112 for selected previous number of hours (in one example,the past 4 hours) (n_(t)=48) for the prediction link 104, and a selectednumber of immediate upstream and immediate downstream links 104 (m=11);continuing with the example above of a 4-hour window, the model analyzesthe 5 immediate upstream and downstream links 104. The PLS regressionmodel 140 thus predicts the average speed of the prediction link 104 forevery five minutes within the next 2 hours (n_(τ)=24).

The PLS-based regression model 140 of this example is trained usinghistorical speed data over a preset period of time, for example 24 days.However, weekends and holidays are excluded from the dataset due topossessing different traffic patterns than on weekdays, producing anumber of days of 17 (n_(d)=17). Hence, in training the model predictormatrix X∈R¹⁷×^((11×48)) is used as the input, and the output variable YE R^(17×48) is constructed based on the speed over the next 2 hours. Thespeed data for a subsequent period of time is then used as the test set.When compared with the ground-truth values of speed and predicted speedfor a representative link 104 during the AM and PM peak periods for acorresponding day, the predicted speed profile is smoother than theground-truth speed profile.

Returning to FIG. 1 , the output dataset 146 generated by the PLSregression model 140 (and representing short-term predicted trafficspeed 148 for each link 104) is provided to the deep learning model 150to generate a further prediction and forecast in an estimated trafficspeed 160 at specific locations within the transportation network 102and at specified times. The deep learning model 150 therefore appliesthe short-term traffic speed predictions 148 for each link 104, augmentsthe dataset with additional information affecting traffic speed overlonger time periods and at different locations, such as weather data113, and generates traffic speed estimates 160 at those specificlocations and specific times.

The deep learning model 150 is comprised at least of one or more machinelearning algorithms that are configured to transform differential timesequences in the response dataset 146, by applying the spatial andtemporal dependencies captured in the partial least squares regressionmodel 140 as inputs to multiple neural networks that generate outputsrepresenting further traffic speed estimates 160 based on the short-termtraffic speed predictions 148. In one aspect of the present invention,these algorithms are embodied in a sequence-to-sequence (Seq2Seq)architecture 152 representing a multi-layered neural network (ormultiple layers of neural networks). The Seq2Seq architecture 152enables integration of weather data 113 with real-time traffic speed 112from link-based probe data 111. The Seq2Seq architecture 152 may includean encoder 154, a decoder 155, and a fully-connected layer 156, asdescribed further herein. FIG. 3 is a schematic illustration of aSeq2Seq architecture 152 for the deep learning model 150, as describedfurther below.

Continuing with the numerical example expressed above, the operation ofthe deep learning model 150 may be further illustrated as follows. Asnoted above, the deep learning model 150 has two data sources: real-timetraffic speed 112 in link-based probe data 111, and weather data 113.Traffic speed 112 and weather data 113 spanning several months are usedin the present example, and as above, weekends and federal holidays havebeen excluded from the datasets. The real-time traffic speed 112 in thelink-based probe data 111 includes 68 features representing historicaltraffic speed 112 from every link 104, while the weather data 113consists of 2 features representing both precipitation and snowaccumulation data for the geographical area 106 representing the links104 being evaluated. There are also 3 additional temporal featuresrepresenting the day, the hour, and the minute of each data point. Thesefeatures are combined into a 2D matrix represented as X∈R^(n×m) where nis the number of time steps, and m is the number of input features. Anoverlapping sliding window approach is applied to convert thismultivariate time series dataset into a supervised format that issuitable for training the deep learning model 150.

In order to assess the impact of weather data 113 on short-termpredicted traffic speed 148, the deep learning model 150 may be trainedusing different types of data across the same historical time sequenceto ascertain accuracy of performance; therefore, in differentimplementations of the present invention utilizing this deep learningmodel 150, different versions of input formats have been compared toassess the most appropriate set of inputs. Accordingly, the followingillustrates an approach to ascertaining a training version of the deeplearning model 150 that produces the most accurate outcome for trafficspeed estimates 160.

In one version of training the deep learning model 150, only historicaltraffic speed data is considered, where inputs consist of 71 features(68 links, and 3 time features). In a second version, historical trafficand weather data are considered, where. The input consists of 73features (68 links, 3 time features, and 2 weather features). A thirdversion considered historical traffic and weather data as well asforecasted weather data, with inputs consisting of 75 features (68links, 3 time features, 2 historical weather features, and 2 forecastedweather features). When used as an experiment in training models, thethird version demonstrated much better performance at capturing theimpact of weather on traffic speed than the first or second versions ofthe input formats.

Returning to FIG. 1 , the Seq2Seq architecture 152 may further includeone or more activation functions 158, which are mathematical constructsthat help the deep learning model 150 and neural network(s) embodiedtherein learn complex patterns within data. Activation functions 150operate to fire the next cell in a neural network, by taking the outputsignal from the previous cell and converting it into some form that canbe taken as input to the next cell. Activation functions 158 aregenerally applied to introduce non-linearity to a neural network.Nevertheless, linear activation functions may also be utilized. Forexample, in one embodiment of the present invention, for fully connectedlayers 156 a ReLU (Rectified Linear Unit) algorithm is applied as theactivation function 158; for the output layer, a different, linearactivation function 158 may be utilized.

The deep learning model 150 generates a traffic speed estimate 160 atone or more locations of the transportation network 102, at one or morespecified times. Such a traffic speed estimate 160 represents a furtherprediction generated by the machine learning models embodied within thetraffic speed modeling framework 100, as a spatially- andtemporally-relative forecast of a traffic speed, estimated for specificlocations within a transportation network 102 at specific times.

These traffic speed estimates 160 may be utilized in the traffic speedmodeling framework 100 in many different applications, and for manydifferent use cases. For example, spatially- and temporally-relativetraffic speed estimates 160 may be used for modeling and forecastingfuture traffic states 180, such as traffic bottleneck prediction 181, inthe transportation network 102 at the specific locations and/or atspecified times. They may also be used to model or forecast any otherfuture traffic state 180 in the transportation network 102 at thespecified times, such as for example volume, flow, and travel time forthe transportation network 102, which may represent congestion at thespecific link 104 due to a slowdown in speed relative to a posted oraverage speed at a specified time.

The traffic speed estimate 160 may be represented as, and considered as,output data 170 that is used for follow-on uses both internal to thetraffic speed modeling framework 100, and external to the traffic speedmodeling framework 100. It is to be understood that there are manyapplications of, and use cases for, traffic speed estimates 160, and isnot to be limited to any one application or any one use casespecifically discussed herein. It is to be further understood that aspatially- and temporally-relative traffic speed estimate 160 is usefulin and of itself for transportation infrastructure planning andmanagement, and may therefore itself by an output of the traffic speedmodeling framework 100, in addition to and separate from any specificoutput delineated herein.

Output data 170 may take many different forms, in addition to providingsignals representing a traffic speed estimate 160 at a particularlocation and/or at a particular time. The traffic speed modelingframework 100 may actuate traffic signaling and messaging systems, forexample by generating signals or instructions for a transportationmanagement system 182. This may include instructions to adjust trafficsignal controllers 183 (for example, to adjust a phase cycle or timingin response to a traffic speed estimate 160). Such signals orinstructions may also include instructions to adjust on-ramp or ingresssignals 184 (for example, in a highway or freeway network having onramps), instructions to generate and broadcast messages for vehicles 185near to the location represented in traffic speed estimate, andinstructions to generate and broadcast messages for public display 186(for example, on message boards positioned proximate to a roadway at ornear a location represented in the traffic speed estimate 160). Othertypes of messaging and alerting is also possible. For example, thepresent invention may be configured to generate route recommendationsfor a planned trip, as well as one or more alternate routerecommendations, based on the forecasts and predictions of the framework100 from traffic speed estimates 160 at specific locations within thetransportation network 102 and at specified times, and any of such routerecommendations may be disseminated through messages and alerts providedto users or user devices.

Other types of outputs and uses of traffic speed estimates 160 are alsopossible, include reporting and analytics 187 (such as incident analysesand alerts) and display and visualization 188 of information generatedby the traffic speed modeling framework 100 on a graphical userinterface, for example to a traffic modeling support tool 190. Yetanother output and use of traffic speed estimates 160 may includerecommendations that prioritize roadway maintenance, such as for examplerouting of snowplows and other clearance and safety equipment in orfollowing adverse weather events to most reduce delay within thetransportation network 102.

Still further, the present invention may be configured to generateinstructions for autonomous control of vehicles within thetransportation network 102 based on route recommendations derived fromtraffic speed estimates 160 at specific locations and at specifiedtimes. Such instructions may be communicated directly to vehicles tocontrol operation of such vehicles, and such vehicles may include anytype of vehicle operating within the transportation network 102,including autonomously-operated passenger vehicles and commercialvehicles such as those delivering packages or other items. Instructionsmay also be provided to remote operators in the case of remote pilotingof such vehicles. Regardless, this embodiment includes instructionsgenerated for autonomous and/or remote control of roadway maintenancevehicles such as snowplows and other clearance and safety equipment inor following adverse weather events, for routing and operation of suchvehicles.

The present invention may also include, as noted above, a trafficmodeling support tool 190, and such a tool 190 is one way that a usermay view and interact with the traffic speed modeling framework 100, andconfigure various aspects thereof. For example, users may configure thetraffic speed modeling framework 100 to manually adjust timing andphases of traffic signals, define links or specific locations in atransportation network 102 that are of interest, define specific timeperiods of interest, and generally manually adjust inputs to the trafficspeed modeling framework 100. Additionally, output data 170 may beprovided directly to the traffic modeling support tool 190.

A user may configure and interact with the traffic speed modelingframework 100 using the traffic modeling support tool 190 via anapplication resident on a computing device, such as a desktop, laptop,tablet, mobile, wearable, or other computer, and/or using a graphicaluser interface. The traffic modeling support tool 190 may includewidgets, drop-down menus, and other indicia presented via theapplication and graphical user interface that enable a user to makeselections and perform functions attendant to operation of the precisiontraffic speed modeling framework 100.

FIG. 2 is a flowchart illustrating steps in a process 200 for performingthe traffic speed modeling framework 100, according to one or moreembodiments of the present invention. Such a process 200 may include, asnoted above one or more functions, mathematical models, algorithms,machine learning processes, and data processing techniques for the dataprocessing elements 132 within such a framework 100, and for the variousfunctions of each element 132.

The process 200 is initialized at step 210 by ingesting input data 110that includes probe data 111 and weather data 113, preparing this inputdata 110 for processing within the machine learning-based models, andinitializing the machine learning-based models for estimating trafficspeed in a transportation network 102. At step 220, variablesrepresenting the predictor space are selected from relevant features ofthe probe data 111 and weather data 113, where appropriate, for eachlink 104 represented in those sets of input data 110. The process 200then initiates the partial least squares (PLS) regression model 140 topredict traffic speed based on the local, historical data contained inthe ingested input data 110 at step 230.

The process 200 also constructs a matrix 144 defined by time steps andvariables selected at step 220 to generate multi-variate time seriesdata 146 at step 240. This may be converted at step 250 into superviseddataset representing each link 104 in the transportation network 102 forfurther application to deep learning model(s) 150, for example usingprocessing techniques such as overlapping sliding windows. At step 260,one or more deep learning models 150 are instantiated by applying thesupervised dataset to train one or more multi-layered neural networks.

The multi-layered neural network may be comprised of a Seq2Seqarchitecture 152 that includes an encoder 154 generating a hidden neuralnetwork state representing the multivariate time-series data, a decoder155 generating an output sequence, and a fully-connected layer 156. Atstep 270, this Seq2Seq architecture 152 transforms differential timesequences in the supervised dataset at least by reducing overfitting toensure that datasets for each time period have the same length, so thatthe decoder 155 can predict a sequence at a next timestep in time. Atstep 280, the multi-layered neural network products an outputrepresented as a traffic speed estimate 160 at one or more locations inthe transportation network 102, and at one or more specified times. Atstep 290, the traffic speed modeling framework 100 and process 200generate forecasts of traffic states 180 such as bottleneck predictions181 from the traffic speed estimates 160.

FIG. 3A and FIG. 3B are schematic illustrations of elements of anexemplary deep learning model 150 in the traffic speed modelingframework 100 having a Seq-to-Seq architecture 152, according to oneembodiment of the present invention. FIG. 3A is an illustration ofencoder 154 and decoder 155 units of the Seq2Seq architecture 152, whileFIG. 2B is a higher-level illustration of a deep learning model 150 thatincludes neural networks comprised of such a Seq2Seq architecture 152having encoders 154 and decoders 155. As noted above, the encoder 152takes in the input time series x 310, and generates a hidden state h 320which in turn is provided to the decoder 155 to generate an outputsequence y 330 as demonstrated in FIG. 3A. The output sequence y 330 ofthe decoder 155 at each time step is then provided to a fully connectedlayer 156 for further processing before producing a final output.

FIG. 3B is as noted above a higher-level illustration of multi-layeredneural network as applied by the deep learning model 150. The deeplearning model 150 includes a memory unit C 340 which is regulated bythree gates: an input gate i 342, a forget gate f 344, and an outputgate o 346. The input gate i 342 controls the contribution of the inputto the memory unit C 340, the forget gate f 344 controls what parts ofthe memory unit C 340 to keep, and the output gate o 346 controls thecontribution of the memory unit C 340 to the output of the LSTM. As withFIG. 3A, the hidden state h 320 represents the output of the encoder 154while the input of the network is represented with s 350. The state ofeach memory unit C 340 is recorded and recurrently self-connected.

The Seq2Seq architecture 152 is, according to one embodiment of thepresent invention, an implementation of Long Short-Term Memory (LSTM)neural networks, which are an adaptation of Recurrent Neural Networks(RNN) designed to model long-term time dependencies in data sequences,which traffic speed 112 exhibits. LSTMs are implemented to avoid thediminishing gradient problem inherent to RNNs, when time series havelong time lags, and produce more accurate estimates of traffic speed.

The state of the memory unit C 340 is recorded and recurrentlyself-connected; the formulations to compute the gates and states aregiven in the following equations:F _(t)=σ(W _(F)·[h _(t-1) ,s _(t)]+b _(F)]),I _(t)=σ(W _(I)·[h _(t-1) ,s _(t)]+b _(I)),C _(t)=tanh(W _(C)·[h _(t-1) ,s _(t)]+b _(C)),C _(t) =F _(t) *C _(t-1) +I _(t) *Ct,O _(t)=σ(WO·[h _(t-1) ,s _(t)]+b _(O)),h _(t) =O _(t)*tanh(C _(t))where C represents the updated state, W is the weights matrix, b is thebias vector for each gate, s_(t) is the input to the network at time t,and * denotes the Hadamard product. The forget gate f 344 reducesoverfitting by controlling how an incoming input contributes to thehidden state h 320. This structure is the main reason why LSTMs do notsuffer from the vanishing gradient problem observed by RNNs.

The encoder 154 and the decoder 155 may each be a separate LSTM-typeneural network. The encoder 154 takes in the input time series, andgenerates a hidden state h 320 which in turn is fed to the decoder 155to generate an output sequence y 330. The output of the decoder 155 ateach time step is then fed to the fully-connected layer 156 for furtherprocessing before producing the final output Onn. This final output ofthe network Onn may then be used in a loss function, considering theground truth output Oact, to further train the multi-layer neuralnetwork(s).

Aspects of the present specification can also be described by thefollowing embodiments:

1. A method of characterizing traffic congestion in a transportationnetwork, comprising:

receiving, as input data, probe data that includes localized, historicallink-based speed information in a transportation network, and weatherdata that includes historical weather conditions and forecasted weatherconditions for a geographical area that includes the transportationnetwork;analyzing the input data in a plurality of data processing elementswithin a computing environment that includes one or more processors andat least one computer-readable non-transitory storage medium havingprogram instructions stored therein which, when executed by the one ormore processors, cause the one or more processors to execute theplurality of data processing elements to characterize spatio-temporaldependencies in traffic speed, by:

-   -   selecting one or more predictors from features in the input data        relevant to each link in the transportation network,    -   constructing a matrix defined by a number of time steps, and a        plurality of features representing temporal characteristics in        each data point, the weather data, and the one or more        predictors selected for each link,    -   generating a multi-variate time-series dataset representing a        predicted short-term traffic speed for each link from the        matrix,    -   transforming differential time sequences in the multi-variate        time-series dataset in a multi-layered neural network having a        sequence-to-sequence architecture comprised of an encoder        generating a hidden neural network state representing the        multivariate time-series data, a decoder generating an output        sequence, and a fully-connected layer that generates an estimate        of traffic speed at one or more locations of the transportation        network at one or more specified times; and        forecasting a traffic state in the transportation network at the        one or more specified times, from the estimate of the traffic        speed, wherein the forecasting a traffic state includes        predicting traffic bottlenecks in the transportation network.        2. The method of claim 1, wherein the traffic bottlenecks        represent predicted delays characterized by a reduction in        traffic speed across a specific distance comprised of one or        more links.        3. The method of claim 1, wherein a forecast of the traffic        state includes a forecast of one or more of volume, flow, and        travel time for the transportation network at the one or more        specified times.        4. The method of claim 1, further comprising generating        visualizations of the one or more of a prediction of traffic        bottlenecks in the transportation network, and a forecast of a        traffic state in the transportation network at one or more        specified times, for a graphical user interface.        5. The method of claim 1, wherein the predictors include traffic        speed features representing link-based speed data from every        link, weather features representing meteorological data        representing precipitation and snow accumulation for a        geographical area having one or more links, and temporal        features representing a day, hour, and minute of each data point        in the link-based speed data.        6. The method of claim 1, wherein the link-based speed        information is segmented based on variances in the        transportation network, the variance including changes in        geometry, intersections, changes in posted speed limit, and lane        configuration.        7. The method of claim 1, wherein the weather data further        includes current weather data and predicted weather conditions        for the geographical area that includes the transportation        network.        8. The method of claim 1, wherein the forecasted weather        conditions include near-term weather forecasts and        extended-range weather forecasts.        9. The method of claim 1, wherein the input data further        includes traffic incident data relative to the transportation        network.        10. The method of claim 1, further comprising applying an        activation function at an output of the multi-layered neural        network to derive the estimate of the traffic speed at the one        or more locations.        11. A method, comprising:        modeling spatio-temporal dependencies in traffic speed from        input data comprised of probe data that includes localized,        historical link-based speed information in a transportation        network, and weather data that includes historical weather        conditions and forecasted weather conditions for a geographical        area that includes the transportation network, by:    -   selecting one or more predictors from features in the input data        relevant to each link in the transportation network,    -   predicting short-term traffic speed for each link, by        constructing a matrix defined by a number of time steps, and a        plurality of features representing temporal characteristics in        each data point, the weather data, and the one or more        predictors for each link, and generating a multi-variate time        series dataset for each link from the matrix,    -   estimating a traffic speed at one or more locations of the        transportation network at one or more specified times, by        applying the multi-variate time-series dataset to a        multi-layered neural network to transform differential time        sequences in the multi-variate time-series dataset, the        multi-layered neural network having a sequence-to-sequence        architecture comprised of an encoder generating a hidden neural        network state representing the multivariate time-series data, a        decoder generating an output sequence, and a fully-connected        layer,        analyzing an estimate of the traffic speed at the one or more        locations of the transportation network to forecast a traffic        state in the transportation network at the one or more specified        times, where traffic state includes traffic bottlenecks        characterized by a reduction in traffic speed across a specific        distance comprised of one or more links in the transportation        network.        12. The method of claim 11, wherein the traffic bottlenecks        represent predicted delays characterized by a reduction in        traffic speed across a specific distance comprised of one or        more links.        13. The method of claim 11, wherein a forecast of the traffic        state includes a forecast of one or more of volume, flow, and        travel time for the transportation network at the one or more        specified times.        14. The method of claim 11, further comprising generating        visualizations of the one or more of a prediction of traffic        bottlenecks in the transportation network, and a forecast of a        traffic state in the transportation network at one or more        specified times, for a graphical user interface.        15. The method of claim 11, wherein the predictors include        traffic speed features representing link-based speed data from        every link, weather features representing meteorological data        representing precipitation and snow accumulation for a        geographical area having one or more links, and temporal        features representing a day, hour, and minute of each data point        in the link-based speed data.        16. The method of claim 11, wherein the link-based speed        information is segmented based on variances in the        transportation network, the variance including changes in        geometry, intersections, changes in posted speed limit, and lane        configuration.        17. The method of claim 11, wherein the weather data further        includes current weather data and predicted weather conditions        for the geographical area that includes the transportation        network.        18. The method of claim 11, wherein the forecasted weather        conditions include near-term weather forecasts and        extended-range weather forecasts.        19. The method of claim 11, wherein the input data further        includes traffic incident data relative to the transportation        network.        20. The method of claim 11, wherein the modeling spatio-temporal        dependencies in traffic speed further comprises applying an        activation function at an output of the multi-layered neural        network to derive the estimate of the traffic speed at the one        or more locations.        21. A system for characterizing traffic congestion in a        transportation network, comprising:        a data collection element configured to receive input data        comprised of probe data that includes localized, historical        link-based speed information in a transportation network, and        weather data that includes historical weather conditions and        forecasted weather conditions for a geographical area that        includes the transportation network;        one or more machine learning models, configured to analyze the        input data to characterize spatio-temporal dependencies in        traffic speed, the one or more machine learning models        including:    -   a partial least squares regression model configured to predict        short-term traffic speed, by        -   selecting one or more predictors from features in the input            data relevant to each link in the transportation network,        -   constructing a matrix defined by a number of time steps, and            a plurality of features representing temporal            characteristics in each data point, the weather data and the            one or more predictors selected for each link to generate a            multi-variate time-series dataset from the matrix            representing a predicted short-term traffic speed for each            link; and    -   a deep learning model configured to transform differential time        sequences in the multi-variate time-series dataset and estimate        a traffic speed at one or more locations of the transportation        network at one or more specified times, the deep learning model        comprised of a multi-layered neural having a        sequence-to-sequence architecture that includes an encoder        generating a hidden neural network state representing the        multivariate time-series data, a decoder generating an output        sequence, and a fully-connected layer,        wherein a forecast of a traffic state in the transportation        network at one or more specified times is generated from the        estimate of the traffic speed at the one or more locations, the        forecast of a traffic state including a prediction of traffic        bottlenecks characterized by a reduction in traffic speed across        a specific distance comprised of one or more links in the        transportation network.        22. The system of claim 21, wherein the prediction of traffic        bottlenecks represents predicted delays characterized by a        reduction in traffic speed across a specific distance comprised        of one or more links.        23. The system of claim 21, wherein the forecast of the traffic        state includes a forecast of one or more of volume, flow, and        travel time for the transportation network at the one or more        specified times.        24. The system of claim 21, further comprising a display element        configured to generate one or more images representing the one        or more of a prediction of traffic bottlenecks in the        transportation network, and a forecast of a traffic state in the        transportation network at one or more specified times, on a        graphical user interface.        25. The system of claim 21, wherein the predictors include        traffic speed features representing link-based speed data from        every link, weather features representing meteorological data        representing precipitation and snow accumulation for a        geographical area having one or more links, and temporal        features representing a day, hour, and minute of each data point        in the link-based speed data.        26. The system of claim 21, wherein the link-based speed        information is segmented based on variances in the        transportation network, the variance including changes in        geometry, intersections, changes in posted speed limit, and lane        configuration.        27. The system of claim 21, wherein the weather data further        includes current weather data and predicted weather conditions        for the geographical area that includes the transportation        network.        28. The system of claim 21, wherein the forecasted weather        conditions include near-term weather forecasts and        extended-range weather forecasts.        29. The system of claim 21, wherein the input data further        includes traffic incident data relative to the transportation        network.        30. The system of claim 21, wherein an activation function is        applied at an output of the multi-layered neural network to        derive the traffic speed at each link.

The systems and methods of the present invention may be implemented inmany different computing environments 130. For example, they may beimplemented in conjunction with a special purpose computer, a programmedmicroprocessor or microcontroller and peripheral integrated circuitelement(s), an ASIC or other integrated circuit, a digital signalprocessor, electronic or logic circuitry such as discrete elementcircuit, a programmable logic device or gate array such as a PLD, PLA,FPGA, PAL, GPU and any comparable means. Still further, the presentinvention may be implemented in cloud-based data processingenvironments, and where one or more types of servers are used to processlarge amounts of data, and using processing components such as CPUs,GPUs, TPUs, and other similar hardware. In general, any means ofimplementing the methodology illustrated herein can be used to implementthe various aspects of the present invention. Exemplary hardware thatcan be used for the present invention includes computers, handhelddevices, telephones (e.g., cellular, Internet enabled, digital, analog,hybrids, and others), and other such hardware. Some of these devicesinclude processors (e.g., a single or multiple microprocessors orgeneral processing units), memory, nonvolatile storage, input devices,and output devices. Furthermore, alternative software implementationsincluding, but not limited to, distributed processing, parallelprocessing, or virtual machine processing can also be configured toperform the methods described herein.

The systems and methods of the present invention may also be wholly orpartially implemented in software that can be stored on a non-transitorycomputer-readable storage medium, executed on programmed general-purposecomputer with the cooperation of a controller and memory, a specialpurpose computer, a microprocessor, or the like. In these instances, thesystems and methods of this invention can be implemented as a programembedded on a mobile device or personal computer through such mediums asan applet, JAVA® or CGI script, as a resource residing on one or moreservers or computer workstations, as a routine embedded in a dedicatedmeasurement system, system component, or the like. The system can alsobe implemented by physically incorporating the system and/or method intoa software and/or hardware system.

Additionally, the data processing functions disclosed herein may beperformed by one or more program instructions stored in or executed bysuch memory, and further may be performed by one or more modulesconfigured to carry out those program instructions. Modules are intendedto refer to any known or later developed hardware, software, firmware,machine learning, artificial intelligence, fuzzy logic, expert system orcombination of hardware and software that is capable of performing thedata processing functionality described herein.

The foregoing descriptions of embodiments of the present invention havebeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseforms disclosed. Accordingly, many alterations, modifications andvariations are possible in light of the above teachings, may be made bythose having ordinary skill in the art without departing from the spiritand scope of the invention. It is therefore intended that the scope ofthe invention be limited not by this detailed description. For example,notwithstanding the fact that the elements of a claim are set forthbelow in a certain combination, it must be expressly understood that theinvention includes other combinations of fewer, more or differentelements, which are disclosed in above even when not initially claimedin such combinations.

The words used in this specification to describe the invention and itsvarious embodiments are to be understood not only in the sense of theircommonly defined meanings, but to include by special definition in thisspecification structure, material or acts beyond the scope of thecommonly defined meanings. Thus if an element can be understood in thecontext of this specification as including more than one meaning, thenits use in a claim must be understood as being generic to all possiblemeanings supported by the specification and by the word itself.

The definitions of the words or elements of the following claims are,therefore, defined in this specification to include not only thecombination of elements which are literally set forth, but allequivalent structure, material or acts for performing substantially thesame function in substantially the same way to obtain substantially thesame result. In this sense it is therefore contemplated that anequivalent substitution of two or more elements may be made for any oneof the elements in the claims below or that a single element may besubstituted for two or more elements in a claim. Although elements maybe described above as acting in certain combinations and even initiallyclaimed as such, it is to be expressly understood that one or moreelements from a claimed combination can in some cases be excised fromthe combination and that the claimed combination may be directed to asub-combination or variation of a sub-combination.

Insubstantial changes from the claimed subject matter as viewed by aperson with ordinary skill in the art, now known or later devised, areexpressly contemplated as being equivalently within the scope of theclaims. Therefore, obvious substitutions now or later known to one withordinary skill in the art are defined to be within the scope of thedefined elements.

The claims are thus to be understood to include what is specificallyillustrated and described above, what is conceptually equivalent, whatcan be obviously substituted and also what essentially incorporates theessential idea of the invention.

The invention claimed is:
 1. A method of characterizing trafficcongestion in a transportation network, comprising: receiving, as inputdata, probe data that includes localized, historical link-based speedinformation in a transportation network, and weather data that includeshistorical weather conditions and forecasted weather conditions for ageographical area that includes the transportation network; analyzingthe input data in a plurality of data processing elements within acomputing environment that includes one or more processors and at leastone computer-readable non-transitory storage medium having programinstructions stored therein which, when executed by the one or moreprocessors, cause the one or more processors to execute the plurality ofdata processing elements to characterize spatio-temporal dependencies intraffic speed, by: selecting one or more predictors from features in theinput data relevant to each link in the transportation network,constructing a matrix defined by a number of time steps, and a pluralityof features representing temporal characteristics in each data point,the weather data, and the one or more predictors selected for each link,generating a multi-variate time-series dataset representing a predictedshort-term traffic speed for each link from the matrix, transformingdifferential time sequences in the multi-variate time-series dataset ina multi-layered neural network having a sequence-to-sequencearchitecture comprised of an encoder generating a hidden neural networkstate representing the multivariate time-series data, a decodergenerating an output sequence, and a fully-connected layer thatgenerates an estimate of traffic speed at one or more locations of thetransportation network at one or more specified times; and forecasting atraffic state in the transportation network at the one or more specifiedtimes, from the estimate of the traffic speed, wherein the forecasting atraffic state includes predicting traffic bottlenecks in thetransportation network, wherein one or more of a traffic signalcontroller is adjusted, and a recommended route is updated, from theestimate of the traffic speed.
 2. The method of claim 1, wherein thetraffic bottlenecks represent predicted delays characterized by areduction in traffic speed across a specific distance comprised of oneor more links.
 3. The method of claim 1, wherein a forecast of thetraffic state includes a forecast of one or more of volume, flow, andtravel time for the transportation network at the one or more specifiedtimes.
 4. The method of claim 1, further comprising generatingvisualizations of the one or more of a prediction of traffic bottlenecksin the transportation network, and a forecast of a traffic state in thetransportation network at one or more specified times, for a graphicaluser interface.
 5. The method of claim 1, wherein the predictors includetraffic speed features representing link-based speed data from everylink, weather features representing meteorological data representingprecipitation and snow accumulation for a geographical area having oneor more links, and temporal features representing a day, hour, andminute of each data point in the link-based speed data.
 6. The method ofclaim 1, wherein the link-based speed information is segmented based onvariances in the transportation network, the variance including changesin geometry, intersections, changes in posted speed limit, and laneconfiguration.
 7. The method of claim 1, wherein the weather datafurther includes current weather data and predicted weather conditionsfor the geographical area that includes the transportation network. 8.The method of claim 1, wherein the forecasted weather conditions includenear-term weather forecasts and extended-range weather forecasts.
 9. Themethod of claim 1, wherein the input data further includes trafficincident data relative to the transportation network.
 10. The method ofclaim 1, further comprising applying an activation function at an outputof the multi-layered neural network to derive the estimate of thetraffic speed at the one or more locations.
 11. A method, comprising:modeling spatio-temporal dependencies in traffic speed from input datacomprised of probe data that includes localized, historical link-basedspeed information in a transportation network, and weather data thatincludes historical weather conditions and forecasted weather conditionsfor a geographical area that includes the transportation network, by:selecting one or more predictors from features in the input datarelevant to each link in the transportation network, predictingshort-term traffic speed for each link, by constructing a matrix definedby a number of time steps, and a plurality of features representingtemporal characteristics in each data point, the weather data, and theone or more predictors for each link, and generating a multi-variatetime series dataset for each link from the matrix, estimating a trafficspeed at one or more locations of the transportation network at one ormore specified times, by applying the multi-variate time-series datasetto a multi-layered neural network to transform differential timesequences in the multi-variate time-series dataset, the multi-layeredneural network having a sequence-to-sequence architecture comprised ofan encoder generating a hidden neural network state representing themultivariate time-series data, a decoder generating an output sequence,and a fully-connected layer; and analyzing an estimate of the trafficspeed at the one or more locations of the transportation network toforecast a traffic state in the transportation network at the one ormore specified times, where the traffic state includes trafficbottlenecks characterized by a reduction in traffic speed across aspecific distance comprised of one or more links in the transportationnetwork, wherein one or more of a traffic signal controller is adjusted,and a recommended route is updated, from the estimate of the trafficspeed.
 12. The method of claim 11, wherein the traffic bottlenecksrepresent predicted delays characterized by a reduction in traffic speedacross a specific distance comprised of one or more links.
 13. Themethod of claim 11, wherein a forecast of the traffic state includes aforecast of one or more of volume, flow, and travel time for thetransportation network at the one or more specified times.
 14. Themethod of claim 11, further comprising generating visualizations of theone or more of a prediction of traffic bottlenecks in the transportationnetwork, and a forecast of a traffic state in the transportation networkat one or more specified times, for a graphical user interface.
 15. Themethod of claim 11, wherein the predictors include traffic speedfeatures representing link-based speed data from every link, weatherfeatures representing meteorological data representing precipitation andsnow accumulation for a geographical area having one or more links, andtemporal features representing a day, hour, and minute of each datapoint in the link-based speed data.
 16. The method of claim 11, whereinthe link-based speed information is segmented based on variances in thetransportation network, the variance including changes in geometry,intersections, changes in posted speed limit, and lane configuration.17. The method of claim 11, wherein the weather data further includescurrent weather data and predicted weather conditions for thegeographical area that includes the transportation network.
 18. Themethod of claim 11, wherein the forecasted weather conditions includenear-term weather forecasts and extended-range weather forecasts. 19.The method of claim 11, wherein the input data further includes trafficincident data relative to the transportation network.
 20. The method ofclaim 11, wherein the modeling spatio-temporal dependencies in trafficspeed further comprises applying an activation function at an output ofthe multi-layered neural network to derive the estimate of the trafficspeed at the one or more locations.
 21. A system for characterizingtraffic congestion in a transportation network, comprising: a datacollection element configured to receive input data comprised of probedata that includes localized, historical link-based speed information ina transportation network, and weather data that includes historicalweather conditions and forecasted weather conditions for a geographicalarea that includes the transportation network; and one or more machinelearning models, configured to analyze the input data to characterizespatio-temporal dependencies in traffic speed, the one or more machinelearning models including: a partial least squares regression modelconfigured to predict short-term traffic speed, by selecting one or morepredictors from features in the input data relevant to each link in thetransportation network, constructing a matrix defined by a number oftime steps, and a plurality of features representing temporalcharacteristics in each data point, the weather data and the one or morepredictors selected for each link to generate a multi-variatetime-series dataset from the matrix representing a predicted short-termtraffic speed for each link, and a deep learning model configured totransform differential time sequences in the multi-variate time-seriesdataset and estimate a traffic speed at one or more locations of thetransportation network at one or more specified times, the deep learningmodel comprised of a multi-layered neural having a sequence-to-sequencearchitecture that includes an encoder generating a hidden neural networkstate representing the multivariate time-series data, a decodergenerating an output sequence, and a fully-connected layer, wherein aforecast of a traffic state in the transportation network at one or morespecified times is generated from the estimate of the traffic speed atthe one or more locations, the forecast of a traffic state including aprediction of traffic bottlenecks characterized by a reduction intraffic speed across a specific distance comprised of one or more linksin the transportation network, and wherein one or more of a trafficsignal controller is adjusted, and a recommended route is updated, fromthe estimate of the traffic speed.
 22. The system of claim 21, whereinthe prediction of traffic bottlenecks represents predicted delayscharacterized by a reduction in traffic speed across a specific distancecomprised of one or more links.
 23. The system of claim 21, wherein theforecast of the traffic state includes a forecast of one or more ofvolume, flow, and travel time for the transportation network at the oneor more specified times.
 24. The system of claim 21, further comprisinga display element configured to generate one or more images representingthe one or more of a prediction of traffic bottlenecks in thetransportation network, and a forecast of a traffic state in thetransportation network at one or more specified times, on a graphicaluser interface.
 25. The system of claim 21, wherein the predictorsinclude traffic speed features representing link-based speed data fromevery link, weather features representing meteorological datarepresenting precipitation and snow accumulation for a geographical areahaving one or more links, and temporal features representing a day,hour, and minute of each data point in the link-based speed data. 26.The system of claim 21, wherein the link-based speed information issegmented based on variances in the transportation network, the varianceincluding changes in geometry, intersections, changes in posted speedlimit, and lane configuration.
 27. The system of claim 21, wherein theweather data further includes current weather data and predicted weatherconditions for the geographical area that includes the transportationnetwork.
 28. The system of claim 21, wherein the forecasted weatherconditions include near-term weather forecasts and extended-rangeweather forecasts.
 29. The system of claim 21, wherein the input datafurther includes traffic incident data relative to the transportationnetwork.
 30. The system of claim 21, wherein an activation function isapplied at an output of the multi-layered neural network to derive thetraffic speed at each link.